Silicon monoxide – a convenient precursor for large scale synthesis of near infrared emitting monodisperse silicon nanocrystals
Bibliographic record
Abstract
While silicon nanocrystals (ncSi) embedded in silicon dioxide thin films have been intensively studied in physics, the potential of batch synthesis of silicon nanocrystals from the solid-state disproportionation of SiO powder has not drawn much attention in chemistry. Herein we describe some remarkable effects observed in the diffraction, microscopy and spectroscopy of SiO powder upon thermal processing in the temperature range 850-1100 °C. Quantum confinement effects and structural changes of the material related to the size of the silicon nanocrystals nucleated and grown in this way were established by Photoluminescence (PL), Raman, FTIR and UV-Visible spectroscopy, PXRD and STEM, pinpointing that the most significant disproportionation transformations happened in the temperature range between 900 and 950 °C. With this know-how a high yield synthesis was developed that produced polydispersions of decyl-capped, hexane-soluble silicon nanocrystals predominantly with near infrared (NIR) PL. Using size-selective precipitation, these polydispersions were separated into monodisperse fractions, which allowed their PL absolute quantum yield (AQY) to be studied as a function of silicon nanocrystal size. This investigation yielded volcano-shaped plots for the AQY confirming the most efficient PL wavelength for ncSi to be located at around 820-830 nm, which corresponded to a size of 3.5-4.0 nm. This work provides opportunities for applications of size-selected near infrared emitting silicon nanocrystals in biomedical imaging and photothermal therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".